Cubit, Consortium Ubiquitous Technologies, is a public-private consortium focused on research and innovation in two business areas: IoT Technologies and Digital Twin. Cubit was founded in 2007 by the Department of Information Engineering at the University of Pisa, Polo Navacchio, and various innovative Italian companies. In 2012, the Aerodynamics Group from the Department of Aerospace Engineering at the University of Pisa also joined the consortium. Cubit’s mission is to combine technological research with industrial expertise to foster continuous innovation and technology transfer, ultimately increasing the competitiveness of companies. The IoT team specializes in the design and development of custom electronic systems for industrial, home automation, and other sectors—covering the entire process from project concept to prototyping and certification.
The FDD team specializes in both numerical and experimental design and analysis in the field of fluid dynamics, supporting various applications such as Automotive, Marine, HVAC, and Aerospace. The team also designs, develops, and tests unmanned vehicles—air, land, and sea—including prototyping and small-scale production. The Cubit personnel involved in the project include both senior and junior profiles, totaling 19 people, 4 of whom are women. The project is managed by Eng. Lorenzo Monti, CTO and former head of hardware design at Cubit. He oversees the activities together with the principal scientist and the program and technical manager of Cubit’s fluid dynamics division, as well as the senior hardware and firmware engineer. They are supported by experts holding PhDs in Industrial Engineering with a specialization in fluid dynamics and CFD, alongside several junior profiles. Communication and dissemination of the project are handled directly by Cubit, which has extensive experience in this field.
ARTES 4.0 – “Advanced Robotics and enabling digital TEchnologies & Systems 4.0” – is a national Competence Center of high specialization funded by the Italian Ministry of Enterprises and Made in Italy (MIMIT). It plays a strategic role in the development of digital technologies and advanced collaborative robotics for Industry 4.0 and 5.0. With 150 members—including companies, universities, and research centers—selected for their expertise and technological excellence, ARTES 4.0 offers a comprehensive range of skills and infrastructures across its fields of interest. With the payoff Science Driven-Innovation, ARTES 4.0 funds and supports science-driven innovation, helping companies in their digital transformation journey. MUSAI is one of the projects funded under the ARTES 4.0 RI&SS Call No. 5. Through this call, ARTES 4.0 supports businesses by providing high-level services through its network of selected members. Project partners 3LOGIC and UNIPI will contribute their expertise in Artificial Intelligence and Machine Learning to help achieve the project’s objectives.
3logic MK was founded in Pisa in 2001 as a craftsmen’s lab, specializing in custom software solutions that combine technological innovation with reliability. The company leverages microservices architectures and interfaces optimized for both mobile and web platforms. Its core areas of expertise include system integration, the development of AI-based applications, and software for the control of drones—both aerial and underwater. Within the MUSAI project, 3logic will be responsible for developing a software platform for real-time analysis of data from onboard cameras and sensors. This platform will utilize AI models specifically trained to optimize result quality while ensuring low energy consumption.
The Department of Information Engineering (DII) at the University of Pisa is a center of excellence for research and education in the fields of Information and Communication Technologies (ICT), Robotics, and Bioengineering. The Remote Sensing and Image Processing research group, part of the DII and involved in the MUSAI project, has extensive experience in processing imagery acquired from passive sensors. The group’s expertise spans from object detection algorithm development to the processing of remotely sensed images, including the analysis and correction of distortions caused by atmospheric propagation. In recent years, the group has specialized in data-driven image analysis strategies using machine learning and deep learning algorithms. As part of the MUSAI project, the DII research group will carry out a feasibility study focused on classifying the health status of marine flora based on images captured by a multispectral camera mounted on an unmanned underwater vehicle.
